HTM theory

Yeldos Dauletkhanuly, Olga Krestinskaya, Alex Pappachen James

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Scopus citations


This chapter presents the general background information about the Hierarchical Temporal Memory (HTM). HTM is a recently proposed cognitive learning algorithm that is intended to emulate the overall structural and functionality of the human neocortex responsible for the high-order functions such as cognition, learning and making predictions. The main properties of HTM is hierarchical structure, sparsity and modularity. HTM consists of two main parts: HTM Spatial Pooler (SP) and HTM Temporal Memory (TM). The HTM SP performs the encoding of the input data and produces sparse distributed representation (SDR) of the input pattern useful for visual data processing and classification tasks. The HTM TM detects the temporal changes in the input data and performs prediction making.
Original languageEnglish (US)
Title of host publicationModeling and Optimization in Science and Technologies
Number of pages12
StatePublished - Jan 1 2020
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2023-09-23

ASJC Scopus subject areas

  • Modeling and Simulation
  • Medical Assisting and Transcription
  • Applied Mathematics


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